Robust speech recognition using the modulation spectrogram
Speech Communication - Special issue on robust speech recognition
Robust automatic speech recognition with missing and unreliable acoustic data
Speech Communication
On the relation between statistical properties of spectrographic masks and recognition accuracy
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
Sparse imputation for large vocabulary noise robust ASR
Computer Speech and Language
Mask estimation for missing data speech recognition based on statistics of binaural interaction
IEEE Transactions on Audio, Speech, and Language Processing
The PASCAL CHiME speech separation and recognition challenge
Computer Speech and Language
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We present an automatic speech recognition system that uses a missing data approach to compensate for challenging environmental noise containing both additive and convolutive components. The unreliable and noise-corrupted (''missing'') components are identified using a Gaussian mixture model (GMM) classifier based on a diverse range of acoustic features. To perform speech recognition using the partially observed data, the missing components are substituted with clean speech estimates computed using both sparse imputation and cluster-based GMM imputation. Compared to two reference mask estimation techniques based on interaural level and time difference-pairs, the proposed missing data approach significantly improved the keyword accuracy rates in all signal-to-noise ratio conditions when evaluated on the CHiME reverberant multisource environment corpus. Of the imputation methods, cluster-based imputation was found to outperform sparse imputation. The highest keyword accuracy was achieved when the system was trained on imputed data, which made it more robust to possible imputation errors.